A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.

The Satellite Application Facility on Land Surface Analysis proposes a land evapotranspiration (ET) product, generated in near-real time. It is produced by an energy balance model forced by radiation components derived from data of the Spinning Enhanced Visible and Infrared Imager aboard Meteosat Second Generation geostationary satellites, at a spatial resolution of approximately 3 km at the equator and covering Europe, Africa, and South America. In this article, we assess the improvement opportunities from moderate spatial resolution satellites for ET monitoring at the Meteosat Second Generation satellite scale. Four variables, namely the land cover, the leaf area index (LAI), the surface albedo, and the open water fraction, derived from moderate-resolution satellites for vegetation monitoring are considered at two spatial resolutions, 1 km and 330 m, corresponding to the imagery provided by Satellite Pour l’Observation de la Terre (SPOT)-VEGETATION and future Project for On-Board Autonomy – Vegetation (PROBA-V) space-borne sensors. The variables are incorporated into the ET model, replacing or complementing input derived from the sensor aboard the geostationary satellite, and their relative effect on the model output is analysed. The investigated processes at small scales unresolved by the geostationary satellite are better taken into account in the final ET estimates, especially over heterogeneous and transition zones. Variables derived from sensors at 250–300 m are shown to have a noticeable effect on the ET estimates compared to the 1 km resolution, demonstrating the interest of PROBA-V 330 m-derived variables for the monitoring of ET at Meteosat Second Generation resolution.

Monitoring soil moisture at the Earth’surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several soil moisture products for continental scale monitoring became available from the main space agencies around the world. Making use of sensors aboard polar satellites sampling in the microwave spectrum, soil moisture can be measured and mapped globally every few days at a spatial resolution as fine as 25 km. However, complementarity of satellite observations is a crucial issue to improve the quality of the estimations provided. In this context, measurements within the visible and infrared from geostationary satellites provide information on the surface from a totally different perspective. In this study, we design a new retrieval algorithm for daily soil moisture monitoring based only on the land surface temperature observations derived from the METEOSAT second generation geostationary satellites. Soil moisture has been retrieved from the retrieval algorithm for an eight years period over Europe and Africa at the SEVIRI sensor spatial resolution (3 km at the sub-satellite point). The results, only available for clear sky and partly cloudy conditions, are for the first time extensively evaluated against in-situ observations provided by the International Soil Moisture Network and FLUXNET at sites across Europe and Africa. The soil moisture retrievals have approximately the same accuracy as the soil moisture products derived from microwave sensors, with the most accurate estimations for semi-arid regions of Europe and Africa, and a progressive degradation of the accuracy towards northern latitudes of Europe. Although some possible improvements can be expected by a better use of other products derived from SEVIRI, the new approach developped and assessed here is a valuable alternative to microwave sensors to monitor daily soil moisture at the resolution of few kilometers over entire continents and could reveal a good complementarity to an improved monitoring system, as the algorithm can produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy conditions (over 10% of the time).

Evaporation of intercepted water by canopies accounts for a non-negligible portion of total land surface evapotranspiration. While monitoring evapotranspiration from space technology is increasingly demanded, most evapotranspiration retrieval algorithms face the problem of providing accurate estimation of evaporation from canopy interception. Because of the operations constraints or of the uncertain quality of the near-real-time rainfall estimates from satellites, the implementation of a diagnostic method is preferred to a dynamical model based on a differential equation ruling the evolution of the water storage on the canopy. In this contribution, an empirical model detecting and quantifying intercepted water on canopies, based on one meteorological variable, is optimized with forecasts from a general circulation model. This diagnostic model of interception is implemented in an evapotranspiration retrieval algorithm using mostly space measurements and its impact is commented.

The land surface models used in numerical weather forecasts and hydrological applications rely on the accuracy of land cover maps available from satellite remote sensing to simulate the energy and water balance at the surface of the Earth. While the impact of classification accuracy on land surface simulations has already been reported, little attention has been paid on the consequences of land cover map uncertainty driven by geolocation accuracy. This impact on the estimated evapotranspiration (ET) from the land surface model H-TESSEL at spatial resolutions ranging from 1 to 30 km is evaluated here, making use of land cover maps at two different initial spatial resolutions, 300 and 1200 m, derived from the GlobCover global product. Geolocation uncertainty affects the land cover maps aggregated at different resolution (from 1.2 to 30 km). However, the effect decreases towards the coarsest resolutions. In addition, aggregated land cover maps are less affected by geolocation errors when the finest original resolution (300 m) is used. The maximum possible effect on ET is quantified over a heterogeneous/transition area in Europe. The result shows an impact up to 10% at 1.2-km resolution to less than 1% at 10-km resolution, at daily timescale, stressing the importance of such issues for kilometre scale applications of land surface models. The use of the highest initial land cover resolution (300 m) reduces by a factor 3 the impact of geolocation on estimated ET at 1.2- to 3-km scale. This study, therefore, stresses on the importance of a careful choice of the land cover map before carrying on land surface model simulations at the kilometre scale.

Testing the sensitivity of regional climate models to the land cover/use changes in temperate regions is a preliminary step in the quantification of the respective contributions from human induced land surface changes and greenhouse gases emissions over Europe. Recently, the LUCAS-FPS (https://ms.hereon.de/cordex_fps_lucas/) initiative from Euro-CORDEX(https://www.euro-cordex.net/) has proposed a common framework and a time varying land cover database (Hoffmann et al., 2022) to build an ensemble of models responding to land use scenarios for the 21st century and ultimately to sort out the signal emerging from the impact of land use changes over Europe. In the meantime, coordinated experiments have been tested at higher resolution over Belgium for national and regional climate services. The present contribution focuses on the plans of coordinated experiments over Belgium (and Europe) with two regional climate models -MAR (Fettweis et al, 2013) and ALARO (Termonia et al, 2018)- : i) the methodological aspects developed to make the simulations comparable and more in line with Euro-Cordex & LUCAS-FPS requirements , ii) the first results from the simulations over these domains by focusing on the changes in key climate variables for the historical past period used for verification and for a scenario of strong land cover change.   Fettweis, X., Franco, B., Tedesco, M., van Angelen, J. H., Lenaerts, J. T. M., van den Broeke, M. R., and Gallée, H.: Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR, The Cryosphere, 7, 469–489, https://doi.org/10.5194/tc-7-469-2013, 2013.   Hoffmann, P., Reinhart, V., Rechid, D. (2021) LUCAS LUC historical land use and land cover change dataset (Version 1.0). World Data Center for Climate (WDCC) at DKRZ.   Termonia, P., Fischer, C., Bazile, E., Bouyssel, F., Brožková, R., Bénard, P., Bochenek, B., Degrauwe, D., Derková, M., El Khatib, R., Hamdi, R., Mašek, J., Pottier, P., Pristov, N., Seity, Y., Smolíková, P., Španiel, O., Tudor, M., Wang, Y., Wittmann, C., and Joly, A.: The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1, Geosci. Model Dev., 11, 257–281, https://doi.org/10.5194/gmd-11-257-2018, 2018.